Solent region: Test Maps
Meghan Kinglsey-Walsh
Last run at: 2021-07-16 17:14:28
1 Mapping the Local Authority
1.1 Mapping Domestic Electricity Consumption
inFile <-paste0(dataFolder, "energy/Subnational_electricity_consumption_statistics_2019.csv")
la_elecData <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## Region = col_character(),
## `Local Authority` = col_character(),
## `LA Code` = col_character(),
## LAU1 = col_character()
## )
## ℹ Use `spec()` for the full column specifications.summary(la_elecData)## Region Local Authority LA Code LAU1
## Length:385 Length:385 Length:385 Length:385
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## E7_meters Standard_meters All_domestic_meters
## Min. : 0.158 Min. : 0.714 Min. : 1.176
## 1st Qu.: 5.242 1st Qu.: 37.416 1st Qu.: 45.499
## Median : 8.810 Median : 52.081 Median : 62.364
## Mean : 39.703 Mean : 247.437 Mean : 287.140
## 3rd Qu.: 14.313 3rd Qu.: 91.393 3rd Qu.: 104.291
## Max. :3942.609 Max. :24769.164 Max. :28711.773
##
## All_Non-domestic_meters Total_meters E7_GWh
## Min. : 0.509 Min. : 1.685 Min. : 0.763
## 1st Qu.: 3.827 1st Qu.: 49.377 1st Qu.: 27.992
## Median : 5.670 Median : 69.070 Median : 47.240
## Mean : 25.636 Mean : 312.776 Mean : 200.460
## 3rd Qu.: 8.569 3rd Qu.: 112.558 3rd Qu.: 71.991
## Max. :2562.836 Max. :31274.609 Max. :19893.136
##
## Standard_GWh All_domestic_GWh All_non-domestic_GWh
## Min. : 4.07 Min. : 7.88 Min. : 9.2
## 1st Qu.: 127.31 1st Qu.: 169.40 1st Qu.: 240.1
## Median : 184.65 Median : 238.49 Median : 349.4
## Mean : 829.09 Mean : 1029.55 Mean : 1691.9
## 3rd Qu.: 294.38 3rd Qu.: 348.68 3rd Qu.: 572.4
## Max. :82843.63 Max. :102736.77 Max. :169804.1
##
## Total_GWh E7_Mean_kWh E7_Mediann_kWh Standard_Mean_kWh
## Min. : 17.09 Min. :3039 Min. :2088 Min. :2655
## 1st Qu.: 426.14 1st Qu.:4620 1st Qu.:3456 1st Qu.:3168
## Median : 582.30 Median :5056 Median :3962 Median :3379
## Mean : 2721.46 Mean :5188 Mean :4036 Mean :3426
## 3rd Qu.: 928.49 3rd Qu.:5668 3rd Qu.:4467 3rd Qu.:3648
## Max. :272540.85 Max. :8245 Max. :7647 Max. :5706
##
## Standard_Median_kWh Domestic_Mean_kWh Domestic_Median_kWh
## Min. :2096 Min. :2756 Min. :2158
## 1st Qu.:2650 1st Qu.:3311 1st Qu.:2703
## Median :2774 Median :3598 Median :2887
## Mean :2785 Mean :3675 Mean :2899
## 3rd Qu.:2925 3rd Qu.:3992 3rd Qu.:3058
## Max. :4553 Max. :6704 Max. :5493
##
## Non-domestic_Mean_kWh Non-domestic_Median_kWh Total_Mean_kWh
## Min. : 18082 Min. : 3317 Min. : 4868
## 1st Qu.: 49493 1st Qu.: 6662 1st Qu.: 7382
## Median : 63188 Median : 7570 Median : 8394
## Mean : 69010 Mean : 7521 Mean : 9503
## 3rd Qu.: 77634 3rd Qu.: 8386 3rd Qu.: 9379
## Max. :297305 Max. :19957 Max. :148254
##
## Total_Median_kWh Average_household_mean_kWh
## Min. :2224 Min. :2872
## 1st Qu.:2774 1st Qu.:3477
## Median :2963 Median :3744
## Mean :2983 Mean :3901
## 3rd Qu.:3146 3rd Qu.:4190
## Max. :5614 Max. :9127
## NA's :3# geo_query <- "https://ons-inspire.esriuk.com/arcgis/rest/services/Administrative_Boundaries/Local_Authority_Districts_December_2018_Boundaries_UK_BGC/MapServer/0/query?where=lad18nm%20IN%20(%27Southampton%27,%27Portsmouth%27,%27Winchester%27,%27Eastleigh%27,%27Isle%20of%20Wight%27,%27Fareham%27,%27Gosport%27,%27Test%20Valley%27,%27East%20Hampshire%27,%27Havant%27,%27New%20Forest%27,%27Hart%27,%27Basingstoke%20and%20Deane%27)&outFields=lad18cd,lad18nm,long,lat&outSR=4326&f=geojson"
# #This only includes the ones we are interested in i.e the Solent
# message("Loading LA geometry from ONS Open Geography API") #Specifying the location message
# la_sf_data <- sf::st_read(geo_query)
# The above is very clever but it takes time
# Load from file
la_shpFile <- paste0(dataFolder, "/boundaries/LA/la_solent.shp")
la_sf_data <- sf::st_read(la_shpFile)## Reading layer `la_solent' from data source
## `/Users/ben/University of Southampton/HCC Energy Landscape Mapping project - Documents/General/data/boundaries/LA/la_solent.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 13 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -1.957224 ymin: 50.57491 xmax: -0.7446835 ymax: 51.38392
## Geodetic CRS: WGS 84head(la_sf_data)## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -1.586543 ymin: 50.57491 xmax: -0.9747962 ymax: 51.38392
## Geodetic CRS: WGS 84
## lad18cd lad18nm long lat
## 1 E06000044 Portsmouth -1.07023 50.8080
## 2 E06000045 Southampton -1.40025 50.9204
## 3 E06000046 Isle of Wight -1.33360 50.6713
## 4 E07000087 Fareham -1.23742 50.8539
## 5 E07000088 Gosport -1.16725 50.8064
## 6 E07000084 Basingstoke and Deane -1.22021 51.2594
## geometry
## 1 MULTIPOLYGON (((-1.071333 5...
## 2 MULTIPOLYGON (((-1.401357 5...
## 3 MULTIPOLYGON (((-1.301876 5...
## 4 MULTIPOLYGON (((-1.269333 5...
## 5 MULTIPOLYGON (((-1.113141 5...
## 6 MULTIPOLYGON (((-0.9861238 ...la_elecData$lad18cd <- la_elecData$`LA Code`#Creating a variable with the local authority code in the same name of the consumption data
la_merged_sf <- merge(la_sf_data, la_elecData)#Merge the local Authority boundaries and the consumption data
ggplot2::ggplot(la_merged_sf) +
geom_sf(aes(fill = Total_GWh))+scale_fill_gradient(low="green",high="red")
1.2 Mapping Transport
1.2.0.1 Freight
inFile <-paste0(dataFolder, "energy/Local Authority level Transport data.csv")
la_Transport <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Code = col_character(),
## Name = col_character(),
## Personal = col_number(),
## Freight = col_number(),
## Total = col_number()
## )summary(la_Transport)## Code Name Personal Freight
## Length:416 Length:416 Min. : 73 Min. : 70
## Class :character Class :character 1st Qu.: 39430 1st Qu.: 18565
## Mode :character Mode :character Median : 62636 Median : 30524
## Mean : 316517 Mean : 173070
## 3rd Qu.: 87103 3rd Qu.: 51128
## Max. :25370982 Max. :13836237
## NA's :18 NA's :18
## Total
## Min. : 143
## 1st Qu.: 58748
## Median : 93167
## Mean : 489587
## 3rd Qu.: 139921
## Max. :39207219
## NA's :18head(la_Transport)## # A tibble: 6 x 5
## Code Name Personal Freight Total
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 W06000019 Blaenau Gwent 18254 7628 25882
## 2 W06000013 Bridgend 65194 30368 95562
## 3 W06000018 Caerphilly 56176 22264 78440
## 4 W06000015 Cardiff 155838 58791 214629
## 5 W06000010 Carmarthenshire 84616 51129 135744
## 6 W06000008 Ceredigion 31141 18534 49675la_Transport$lad18cd <- la_Transport$Code # Creating a variable with the local authority code in the same name of the consumption data
la_merged_Transp <- merge(la_sf_data, la_Transport)# Merge the local Authority boundaries and the Transport data
ggplot2::ggplot(la_merged_Transp) +
geom_sf(aes(fill = Freight)) +
scale_fill_gradient(name = "Freight: Thousand tonnes of oil equivalent (ktoe)", low="green",high="red")
1.2.0.2 Personal
Note: this data is not per household/person so it will correlate (to some extent) with population size.
# re-use the merged dataset
ggplot2::ggplot(la_merged_Transp) +
geom_sf(aes(fill = Personal)) +
scale_fill_gradient(name = "Personal: Thousand tonnes of oil equivalent (ktoe)", low="green",high="red") 
## Mapping Fuel Poverty
### 2021 Proportion of Population
inFile<-paste0(dataFolder, "Fuel Poverty/2021 LA_Fuel_Poverty.csv")
la_fuelpov_2021<-readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Area Codes` = col_character(),
## `Number of households1` = col_number(),
## `Number of households in fuel poverty1` = col_number(),
## `Proportion of households fuel poor (%)` = col_character()
## )head(la_fuelpov_2021)## # A tibble: 6 x 4
## `Area Codes` `Number of househ… `Number of households … `Proportion of househ…
## <chr> <dbl> <dbl> <chr>
## 1 E92000001 23661751 3175979 13.40
## 2 E12000001 1191912 176869 14.80
## 3 E06000047 235959 36620 15.50
## 4 E06000005 49204 6881 14.00
## 5 E06000001 42645 6292 14.80
## 6 E06000002 60318 9966 16.50la_fuelpov_2021$lad18cd <- la_fuelpov_2021$`Area Codes` # Creating a variable with the local authority code in the same name
la_merged_Fuelpov2021 <- merge(la_sf_data, la_fuelpov_2021)# Merge the local Authority boundaries and the Fuel Poverty data
la_merged_Fuelpov2021$pcFP <- as.numeric(la_merged_Fuelpov2021$`Proportion of households fuel poor (%)`) # convert to numeric from char - why does it load as char??
ggplot2::ggplot(la_merged_Fuelpov2021) +
geom_sf(aes(fill = pcFP)) +
scale_fill_gradient(name = "Proportion of households fuel poor (%)", low="green",high="red")
2 Mapping the Middle Layer Super Output Areas (MSOA)
2.1 Mapping Domestic
2.1.1 Mapping the Electricity Consumption
# electricity consumption data at MSOA level (pre downloaded)
inFile <-paste0(dataFolder, "energy/MSOA_DOM_ELEC_2019.csv")
msoa_elecData <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Number of meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double()
## )head(msoa_elecData)## # A tibble: 6 x 8
## `Local Authority N… `Local Authority … `MSOA Name` `Middle Layer Super Outpu…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool … E02002483
## 2 Hartlepool E06000001 Hartlepool … E02002484
## 3 Hartlepool E06000001 Hartlepool … E02002485
## 4 Hartlepool E06000001 Hartlepool … E02002487
## 5 Hartlepool E06000001 Hartlepool … E02002488
## 6 Hartlepool E06000001 Hartlepool … E02002489
## # … with 4 more variables: Number of meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>inf <-paste0(dataFolder, "boundaries/MSOA/msoa_solent.shp")
message("Loading Middle Layer Super Output Area (MSOA) from file")## Loading Middle Layer Super Output Area (MSOA) from file#When using shp files you might also need to have other supplementary files in the same folder to allow it to run https://cfss.uchicago.edu/notes/simple-features/
msoa_sf_data <- sf::read_sf(inf)
head (msoa_sf_data)## Simple feature collection with 6 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 458985.4 ymin: 102894 xmax: 467725.4 ymax: 107035
## Projected CRS: OSGB 1936 / British National Grid
## # A tibble: 6 x 14
## MSOA11CD MSOA11NM OBJECTID BNG_E BNG_N LONG_ LAT Shape_Leng Shape__Are
## <chr> <chr> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 E02003524 Portsmouth… 3510 463726 106331 -1.10 50.9 8175. 1880016.
## 2 E02003525 Portsmouth… 3511 465172 105904 -1.08 50.8 13220. 2012966.
## 3 E02003526 Portsmouth… 3512 466581 106095 -1.06 50.9 10425. 2260228.
## 4 E02003527 Portsmouth… 3513 464176 104420 -1.09 50.8 21957. 3838768.
## 5 E02003529 Portsmouth… 3514 466420 104925 -1.06 50.8 10636. 1499618.
## 6 E02003530 Portsmouth… 3515 465411 103721 -1.07 50.8 13264. 1695461.
## # … with 5 more variables: Shape__Len <dbl>, LAD11CD <chr>, LAD11NM <chr>,
## # nOAs <int>, geometry <MULTIPOLYGON [m]>table(msoa_sf_data$LAD11NM)#how many MSOAs are in each LA##
## Basingstoke and Deane East Hampshire Eastleigh
## 22 15 15
## Fareham Gosport Hart
## 14 10 11
## Havant Isle of Wight New Forest
## 17 18 23
## Portsmouth Southampton Test Valley
## 25 32 15
## Winchester
## 14msoa_elecData$MSOA11CD <- msoa_elecData$ 'Middle Layer Super Output Area (MSOA) Code' #Creating a variable with the LA code and the name as in the sf_data
msoa_merged_sf <-merge(msoa_sf_data, msoa_elecData)
ggplot2::ggplot(msoa_merged_sf) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Electricity: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all MSOAs)")
#mapping it2.1.2 Mapping the Gas
# gas consumption data at MSOA level (pre downloaded)
inFile <-paste0(dataFolder, "energy/MSOA_GAS_2019.csv")
msoa_gasData <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Number of consuming meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double(),
## `Number of non consuming meters` = col_character()
## )head(msoa_gasData)## # A tibble: 6 x 9
## `Local Authority N… `Local Authority … `MSOA Name` `Middle Layer Super Outpu…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool … E02002483
## 2 Hartlepool E06000001 Hartlepool … E02002484
## 3 Hartlepool E06000001 Hartlepool … E02002485
## 4 Hartlepool E06000001 Hartlepool … E02002487
## 5 Hartlepool E06000001 Hartlepool … E02002488
## 6 Hartlepool E06000001 Hartlepool … E02002489
## # … with 5 more variables: Number of consuming meters <dbl>,
## # Consumption (kWh) <dbl>, Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>,
## # Number of non consuming meters <chr>msoa_gasData$MSOA11CD <- msoa_gasData$ 'Middle Layer Super Output Area (MSOA) Code'
msoa_merged_sf_domgas <-merge(msoa_sf_data, msoa_gasData)
ggplot2::ggplot(msoa_merged_sf_domgas) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all MSOAs)")
#mapping itDoes this match to non-gas areas??
2.2 Mapping Commercial
2.2.1 Mapping Electricty
# electricity consumption data for industry at MSOA level (pre downloaded)
inFile <-paste0(dataFolder, "energy/MSOA_NONDOM_2019.csv")
msoa_elecDatanondom <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Number of meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double()
## )head(msoa_elecDatanondom)## # A tibble: 6 x 8
## `Local Authority N… `Local Authority … `MSOA Name` `Middle Layer Super Outpu…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool … E02002483
## 2 Hartlepool E06000001 Hartlepool … E02002484
## 3 Hartlepool E06000001 Hartlepool … E02002485
## 4 Hartlepool E06000001 Hartlepool … E02002487
## 5 Hartlepool E06000001 Hartlepool … E02002488
## 6 Hartlepool E06000001 Hartlepool … E02002489
## # … with 4 more variables: Number of meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>msoa_elecDatanondom$MSOA11CD <- msoa_elecDatanondom$ 'Middle Layer Super Output Area (MSOA) Code'
msoa_merged_sf_nondom <-merge(msoa_sf_data, msoa_elecDatanondom)
ggplot2::ggplot(msoa_merged_sf_nondom) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Electricity: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all MSOAs)")
#mapping it2.2.2 Mapping Gas
# gas consumption data for industry at MSOA level (pre downloaded)
inFile <-paste0(dataFolder, "energy/MSOA_NonDom_Gas_2019.csv")
msoa_gasDatanondom <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Number of meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double()
## )head(msoa_gasDatanondom)## # A tibble: 6 x 8
## `Local Authority N… `Local Authority … `MSOA Name` `Middle Layer Super Outpu…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool … E02002483
## 2 Hartlepool E06000001 Hartlepool … E02002484
## 3 Hartlepool E06000001 Hartlepool … E02002485
## 4 Hartlepool E06000001 Hartlepool … E02002487
## 5 Hartlepool E06000001 Hartlepool … E02002488
## 6 Hartlepool E06000001 Hartlepool … E02002489
## # … with 4 more variables: Number of meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>msoa_gasDatanondom$MSOA11CD <- msoa_gasDatanondom$ 'Middle Layer Super Output Area (MSOA) Code'
msoa_merged_sf_nondom_gas <-merge(msoa_sf_data, msoa_gasDatanondom)
ggplot2::ggplot(msoa_merged_sf_nondom_gas) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all MSOAs)")
#mapping itThe holes tell you where there is no non-domestic gas use
3 Mapping the Lower Layer Super Output Area (LSOA)
3.1 Mapping Domestic
3.1.1 Mapping Electricity consumption
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/LSOA_ELEC_2019.csv")
lsoa_elecData <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `Middle Layer Super Output Area (MSOA) Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Lower Layer Super Output Area (LSOA) Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Total number of domestic electricity meters` = col_double(),
## `Total domestic electricity consumption (kWh)` = col_double(),
## `Mean domestic electricity consumption
## (kWh per meter)` = col_double(),
## `Median domestic electricity consumption
## (kWh per meter)` = col_double()
## )lsoa_elecData$LSOA11CD <- lsoa_elecData$`Lower Layer Super Output Area (LSOA) Code`
head(msoa_elecData)## # A tibble: 6 x 9
## `Local Authority N… `Local Authority … `MSOA Name` `Middle Layer Super Outpu…
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool … E02002483
## 2 Hartlepool E06000001 Hartlepool … E02002484
## 3 Hartlepool E06000001 Hartlepool … E02002485
## 4 Hartlepool E06000001 Hartlepool … E02002487
## 5 Hartlepool E06000001 Hartlepool … E02002488
## 6 Hartlepool E06000001 Hartlepool … E02002489
## # … with 5 more variables: Number of meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>, MSOA11CD <chr>#The LSOA boundaries for the Solent have been pre-downloaded
inf<-paste0(dataFolder, "boundaries/LSOA/lsoa_solent.shp")
message ("Loading LSOA boundaries from file")## Loading LSOA boundaries from filelsoa_sf_data <- sf::read_sf(inf)
head(lsoa_sf_data)## Simple feature collection with 6 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 465991.4 ymin: 100849 xmax: 467770.5 ymax: 102360.8
## Projected CRS: OSGB 1936 / British National Grid
## # A tibble: 6 x 16
## LSOA11CD OBJECTID LSOA11NM LSOA11NMW BNG_E BNG_N LONG LAT Shape__Are
## <chr> <int> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 E01017013 16519 Portsmout… Portsmouth… 466127 101741 -1.06 50.8 145134.
## 2 E01017014 16520 Portsmout… Portsmouth… 466924 101957 -1.05 50.8 757371.
## 3 E01017015 16521 Portsmout… Portsmouth… 467084 101499 -1.05 50.8 472647.
## 4 E01017016 16522 Portsmout… Portsmouth… 466330 101597 -1.06 50.8 122927.
## 5 E01017017 16523 Portsmout… Portsmouth… 466813 101069 -1.05 50.8 166924.
## 6 E01017018 16524 Portsmout… Portsmouth… 466350 101184 -1.06 50.8 184945.
## # … with 7 more variables: Shape__Len <dbl>, MSOA11CD <chr>, MSOA11NM <chr>,
## # LAD11CD <chr>, LAD11NM <chr>, nLSOAs <int>, geometry <MULTIPOLYGON [m]>table(lsoa_sf_data$LAD11NM)#How many LSOAs are there in each LA##
## Basingstoke and Deane East Hampshire Eastleigh
## 109 72 77
## Fareham Gosport Hart
## 73 53 57
## Havant Isle of Wight New Forest
## 78 89 114
## Portsmouth Southampton Test Valley
## 125 148 71
## Winchester
## 70lsoa_merged_sf <- merge(lsoa_sf_data, lsoa_elecData) #merging the boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all LSOAs)")
#Mapping it3.1.1.1 Looking at cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Southampton")
# plotting the electricity consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
3.1.2 Mapping Gas
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/LSOA_GAS_2019.csv")
lsoa_gasData <- readr::read_csv(inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `LSOA Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Number of consuming meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double(),
## `Number of non-consuming meters` = col_character()
## )lsoa_gasData$LSOA11CD <- lsoa_gasData$`Lower Layer Super Output Area (LSOA) Code`
head(lsoa_gasData)## # A tibble: 6 x 12
## `Local Authority… `Local Authority… `MSOA Name` `Middle Layer Sup… `LSOA Name`
## <chr> <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 2 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 3 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 4 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 5 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## 6 Hartlepool E06000001 Hartlepool… E02002483 Hartlepool…
## # … with 7 more variables: Lower Layer Super Output Area (LSOA) Code <chr>,
## # Number of consuming meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>,
## # Number of non-consuming meters <chr>, LSOA11CD <chr>lsoa_merged_sf_gas <- merge(lsoa_sf_data, lsoa_gasData) #merging the boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf_gas) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all LSOAs)")
#Mapping itAha - this one shows where there is no gas :-)
3.1.3 Mapping the Indices of Multiple Deprivation
# Multiple deprivation data at MSOA pre downloaded
inFile <-paste0(dataFolder, "Indices of Multiple Deprivation/Indices_of_Multiple_Deprivation_(IMD)_2019.csv")
lsoa_multidep <- readr::read_csv (inFile)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## lsoa11cd = col_character(),
## lsoa11nm = col_character(),
## lsoa11nmw = col_character(),
## LSOA01NM = col_character(),
## LADcd = col_character(),
## LADnm = col_character()
## )
## ℹ Use `spec()` for the full column specifications.head(lsoa_multidep)## # A tibble: 6 x 66
## FID lsoa11cd lsoa11nm lsoa11nmw st_areasha st_lengths IMD_Rank IMD_Decile
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 E01000816 Bromley … Bromley 0… 299021. 3337. 19874 7
## 2 2 E01000818 Bromley … Bromley 0… 1292309. 7408. 20548 7
## 3 3 E01000819 Bromley … Bromley 0… 397713. 4384. 9866 4
## 4 4 E01000820 Bromley … Bromley 0… 587458. 4838. 13461 5
## 5 5 E01000822 Bromley … Bromley 0… 218524. 2738. 20757 7
## 6 6 E01000823 Bromley … Bromley 0… 242692. 3195. 22675 7
## # … with 58 more variables: LSOA01NM <chr>, LADcd <chr>, LADnm <chr>,
## # IMDScore <dbl>, IMDRank0 <dbl>, IMDDec0 <dbl>, IncScore <dbl>,
## # IncRank <dbl>, IncDec <dbl>, EmpScore <dbl>, EmpRank <dbl>, EmpDec <dbl>,
## # EduScore <dbl>, EduRank <dbl>, EduDec <dbl>, HDDScore <dbl>, HDDRank <dbl>,
## # HDDDec <dbl>, CriScore <dbl>, CriRank <dbl>, CriDec <dbl>, BHSScore <dbl>,
## # BHSRank <dbl>, BHSDec <dbl>, EnvScore <dbl>, EnvRank <dbl>, EnvDec <dbl>,
## # IDCScore <dbl>, IDCRank <dbl>, IDCDec <dbl>, IDOScore <dbl>, IDORank <dbl>,
## # IDODec <dbl>, CYPScore <dbl>, CYPRank <dbl>, CYPDec <dbl>, ASScore <dbl>,
## # ASRank <dbl>, ASDec <dbl>, GBScore <dbl>, GBRank <dbl>, GBDec <dbl>,
## # WBScore <dbl>, WBRank <dbl>, WBDec <dbl>, IndScore <dbl>, IndRank <dbl>,
## # IndDec <dbl>, OutScore <dbl>, OutRank <dbl>, OutDec <dbl>, TotPop <dbl>,
## # DepChi <dbl>, Pop16_59 <dbl>, Pop60_ <dbl>, WorkPop <dbl>,
## # Shape__Area <dbl>, Shape__Length <dbl>lsoa_multidep$LSOA11CD <- lsoa_multidep$lsoa11cd #creating a variable with the LSOA code in the same name as in sf_data
head(lsoa_multidep)## # A tibble: 6 x 67
## FID lsoa11cd lsoa11nm lsoa11nmw st_areasha st_lengths IMD_Rank IMD_Decile
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 E01000816 Bromley … Bromley 0… 299021. 3337. 19874 7
## 2 2 E01000818 Bromley … Bromley 0… 1292309. 7408. 20548 7
## 3 3 E01000819 Bromley … Bromley 0… 397713. 4384. 9866 4
## 4 4 E01000820 Bromley … Bromley 0… 587458. 4838. 13461 5
## 5 5 E01000822 Bromley … Bromley 0… 218524. 2738. 20757 7
## 6 6 E01000823 Bromley … Bromley 0… 242692. 3195. 22675 7
## # … with 59 more variables: LSOA01NM <chr>, LADcd <chr>, LADnm <chr>,
## # IMDScore <dbl>, IMDRank0 <dbl>, IMDDec0 <dbl>, IncScore <dbl>,
## # IncRank <dbl>, IncDec <dbl>, EmpScore <dbl>, EmpRank <dbl>, EmpDec <dbl>,
## # EduScore <dbl>, EduRank <dbl>, EduDec <dbl>, HDDScore <dbl>, HDDRank <dbl>,
## # HDDDec <dbl>, CriScore <dbl>, CriRank <dbl>, CriDec <dbl>, BHSScore <dbl>,
## # BHSRank <dbl>, BHSDec <dbl>, EnvScore <dbl>, EnvRank <dbl>, EnvDec <dbl>,
## # IDCScore <dbl>, IDCRank <dbl>, IDCDec <dbl>, IDOScore <dbl>, IDORank <dbl>,
## # IDODec <dbl>, CYPScore <dbl>, CYPRank <dbl>, CYPDec <dbl>, ASScore <dbl>,
## # ASRank <dbl>, ASDec <dbl>, GBScore <dbl>, GBRank <dbl>, GBDec <dbl>,
## # WBScore <dbl>, WBRank <dbl>, WBDec <dbl>, IndScore <dbl>, IndRank <dbl>,
## # IndDec <dbl>, OutScore <dbl>, OutRank <dbl>, OutDec <dbl>, TotPop <dbl>,
## # DepChi <dbl>, Pop16_59 <dbl>, Pop60_ <dbl>, WorkPop <dbl>,
## # Shape__Area <dbl>, Shape__Length <dbl>, LSOA11CD <chr>lsoa_multidep_merged_sf <- merge(lsoa_sf_data, lsoa_multidep) #merging these
ggplot2::ggplot(lsoa_multidep_merged_sf) +
geom_sf(aes(fill=`IMDScore`))+ # which data set has the scores?
scale_fill_continuous(name="IMD Score", low="green",high="red")+
labs(caption ="Solent (all MSOAs)")
3.1.4 Mapping Fuel Poverty
3.1.4.1 2021 Fuel Poverty
inFile<-paste0(dataFolder, "Fuel Poverty/2021 LSOA_Fuel_Poverty.csv")
lsoa_fuelpov_2021<-readr::read_csv (inFile)## Warning: Missing column names filled in: 'X9' [9]##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `LSOA Code` = col_character(),
## `LSOA Name` = col_character(),
## `LA Code` = col_character(),
## `LA Name` = col_character(),
## Region = col_character(),
## `Number of households1` = col_number(),
## `Number of households in fuel poverty1` = col_double(),
## `Proportion of households fuel poor (%)` = col_double(),
## X9 = col_logical()
## )## Warning: 1 parsing failure.
## row col expected actual file
## 32845 Proportion of households fuel poor (%) a double England '/Users/ben/University of Southampton/HCC Energy Landscape Mapping project - Documents/General/data/Fuel Poverty/2021 LSOA_Fuel_Poverty.csv'head(lsoa_fuelpov_2021)## # A tibble: 6 x 9
## `LSOA Code` `LSOA Name` `LA Code` `LA Name` Region `Number of househ…
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 E01000001 City of London … E09000001 City of Lond… London 928
## 2 E01000002 City of London … E09000001 City of Lond… London 882
## 3 E01000003 City of London … E09000001 City of Lond… London 878
## 4 E01000005 City of London … E09000001 City of Lond… London 502
## 5 E01000006 Barking and Dag… E09000002 Barking and … London 587
## 6 E01000007 Barking and Dag… E09000002 Barking and … London 646
## # … with 3 more variables: Number of households in fuel poverty1 <dbl>,
## # Proportion of households fuel poor (%) <dbl>, X9 <lgl>lsoa_fuelpov_2021$LSOA11CD <- lsoa_fuelpov_2021$"LSOA Code" #creating a variable with the LSOA code in the same name as in sf_data
lsoa_fuelpov_2021_merged <- merge(lsoa_sf_data, lsoa_fuelpov_2021) #merging these
# as above - why does it load as a char?
lsoa_fuelpov_2021_merged$pcFP <- as.numeric(lsoa_fuelpov_2021_merged$`Proportion of households fuel poor (%)`)
ggplot2::ggplot(lsoa_fuelpov_2021_merged) +
geom_sf(aes(fill=pcFP))+
scale_fill_continuous(name="Proportion of households fuel poor (%)", low="green",high="red")+
labs(caption ="Solent (all LSOAs)")
4 The end
R.Version()## $platform
## [1] "x86_64-apple-darwin17.0"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "darwin17.0"
##
## $system
## [1] "x86_64, darwin17.0"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.2"
##
## $year
## [1] "2020"
##
## $month
## [1] "06"
##
## $day
## [1] "22"
##
## $`svn rev`
## [1] "78730"
##
## $language
## [1] "R"
##
## $version.string
## [1] "R version 4.0.2 (2020-06-22)"
##
## $nickname
## [1] "Taking Off Again"